code: def forward(self, inputs, mask=None):
      # current_inputs shape [b, s, h]
      self.input_shape_var = inputs.size()[1:]
      hidden = self.fc_layer1(inputs)  # [b, s, h']
      hidden = self.fc_layer1_activation(hidden)
      hidden = self.fc_layer2(hidden)  # [b, s, 1]
      attention = F.softmax(hidden, dim=1)
      gated_inputs = torch.sum(attention * inputs, dim=1)
      self.output_shape_var = gated_inputs.size()[1:]
      return gated_inputs
  
imports: import logging
  import torch
  from torch import nn
  from torch.nn import functional
  from ludwig.utils.torch_utils import get_activation, LudwigModule
label_desc: Use image node_id 2 for calling the FeedForwardAttentionReducer obj's underlying member method code with example usage: obj.forward(inputs, mask) and returns: gated_inputs